The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Credit scoring has obtained more and more attention as the credit industry can benefit from reducing potential risks. Hence, many different useful techniques, known as the credit scoring models, have been developed by the banks and researchers in order to solve the problems involved during the evaluation process. In this paper, a hybrid credit scoring model (HCSM) is developed to deal with the credit...
In this study, we propose a least squares bilateral-weighted fuzzy support vector machine (LS-BFSVM) method to evaluate the credit risk problem. The method can not only reduce the computational complexity by considering equality constraints instead of inequalities for the classification problem with a formulation in least squares sense, but also increase the training algorithm's generalization ability...
In SVM ensemble learning, diversity strategy is one of the most important determinants to obtain good performance. In order to examine and analyze the impacts of diversity strategies on SVM ensemble learning, this study tries to make such a deep investigation by taking credit scoring as an illustrative example. Experimental results found that the accuracy of ensemble models will be increased if ensemble...
Financial diagnosis is an important and widely studied topic in the last three decades. Recently, the support vector machine (SVM) has been applied to the problem of financial diagnosis. Fuzzy c-means clustering (FCM) is among considerable techniques for data reduction. In addition, principal component analysis (PCA) is a powerful technique for feather extraction. This paper proposes using fuzzy c-means...
Support vector machines (SVMs) are provided with great abilities of analyzing data with the characteristics of small sample-sets, high dimension, nonlinear, high noise. They are applicable to deal with machine learning problems of industries. The paper brought forward to taking advantage of multi-classification SVMs to evaluate the sensory qualities of products according to the data feature of such...
Utilizing the ability simple in application and quick in convergence of quantum delta-potential-well-based particle swarm optimization (QDPSO) algorithm and the high generalization ability of support vector machine (SVM), selecting the appropriate state variables, a dynamic time-varying model has been built. Using the model and algorithm to per-estimate some biochemical state variables which can not...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.